code: def finalize_minibatch(
      self,
      accumulated_minibatch_data: Dict[str, Any],
      device: Union[str, torch.device],
  ) -> Dict[str, Any]:
      return {
          "encoder_mb_data": self.__gnn_model.finalize_minibatch(
              accumulated_minibatch_data["encoder_mb_data"],
              device=device,
          ),
          "decoder_mb_data": self.__decoder_model.finalize_minibatch(
              accumulated_minibatch_data["decoder_mb_data"],
              device=device,
          ),
      }
  
imports: from typing_extensions import Final, TypedDict
  import torch
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
  from ptgnn.neuralmodels.gnn.structs import GnnOutput, GraphData, TensorizedGraphData
  from ptgnn.neuralmodels.reduceops import AbstractVarSizedElementReduce, ElementsToSummaryRepresentationInput, MultiheadSelfAttentionVarSizedElementReduce, SimpleVarSizedElementReduce
  from ptgnn.neuralmodels.sequence.grucopydecoder import DecoderData, GruCopyingDecoder, GruCopyingDecoderModel, TokenizedOutput
label_desc: Use image node_id 7 for calling the Graph2Seq obj's underlying member method code with example usage: obj.finalize_minibatch(accumulated_minibatch_data, device) and returns: dict